{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别与计算机视觉文档实践\n",
    "\n",
    ">* 本周主要内容：人脸（Face） API文档不同平台对比与实践及计算机视觉入门（认知服务）\n",
    ">* 202009_API_人工智能与机器学习_week03\n",
    ">*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 复习\n",
    "\n",
    "* 上周主要内容： \n",
    ">    * 1、API文档\n",
    ">    * 2、认知服务-人脸识别\n",
    ">    * 3、pandas黑魔法（json_normalize）\n",
    "-----\n",
    "* 提问、检查与扩展：\n",
    ">    * 1、上周的Azure face API随机检查2位同学，对于API文档是否有能力详细阅读？\n",
    ">    * 2、抽查2位同学，对于获取的数据用途有何思考？是否尝试其他平台（XXX API、XXX API）      \n",
    ">    * 3、扩展: 我们尝试抽取了4张图片，包含AB、ABC、BCD、ABCD四个人物特征，如何通过API检查数据差异？\n",
    "  \n",
    "\n",
    "-----\n",
    "## 本周学习目标：\n",
    "\n",
    "> $\\mathcal{1、Azure 认知服务-人脸集合演示}$     \n",
    "> $\\mathcal{2、Face++ 之 FaceSets 实践}$     \n",
    "> $\\mathcal{3、计算机视觉实践}$    \n",
    "\n",
    "\n",
    "\n",
    "## 学生权限\n",
    "\n",
    "* [访问学生权益](https://azure.microsoft.com/zh-cn/education/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #effee2;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #effee2;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复习\n",
    "\n",
    "> 1、回顾阅读API文档的关键点     \n",
    "> 2、正确阅读json数据 [jsonviewer.stack.hu(json转换检查数据)](http://jsonviewer.stack.hu)    \n",
    "> 3、pandas 中的json_normalize模块/函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"cac77619-4e43-4564-8d56-cb0283c3b3a4\", \"faceRectangle\": {\"top\": 118, \"left\": 144, \"width\": 88, \"height\": 88}, \"faceAttributes\": {\"smile\": 0.813, \"headPose\": {\"pitch\": -3.4, \"roll\": 1.4, \"yaw\": 9.4}, \"gender\": \"male\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.003, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.813, \"neutral\": 0.184, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.21}, \"exposure\": {\"exposureLevel\": \"overExposure\", \"value\": 0.81}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.01}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.18, \"invisible\": false, \"hairColor\": [{\"color\": \"brown\", \"confidence\": 0.95}, {\"color\": \"black\", \"confidence\": 0.93}, {\"color\": \"other\", \"confidence\": 0.23}, {\"color\": \"blond\", \"confidence\": 0.23}, {\"color\": \"gray\", \"confidence\": 0.21}, {\"color\": \"red\", \"confidence\": 0.15}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"ab8b0b55-ff58-477b-82fd-a822c453403e\", \"faceRectangle\": {\"top\": 117, \"left\": 376, \"width\": 64, \"height\": 64}, \"faceAttributes\": {\"smile\": 0.456, \"headPose\": {\"pitch\": -1.1, \"roll\": -0.2, \"yaw\": 6.6}, \"gender\": \"female\", \"age\": 22.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.001, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.456, \"neutral\": 0.542, \"sadness\": 0.001, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.16}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.58}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.01}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.08, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"other\", \"confidence\": 0.63}, {\"color\": \"brown\", \"confidence\": 0.46}, {\"color\": \"gray\", \"confidence\": 0.39}, {\"color\": \"blond\", \"confidence\": 0.03}, {\"color\": \"red\", \"confidence\": 0.01}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a\", \"faceRectangle\": {\"top\": 41, \"left\": 676, \"width\": 52, \"height\": 52}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": 3.0, \"roll\": -1.3, \"yaw\": -0.6}, \"gender\": \"male\", \"age\": 25.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"ReadingGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.0}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.66}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.07}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [{\"type\": \"glasses\", \"confidence\": 1.0}], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.06, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 0.98}, {\"color\": \"brown\", \"confidence\": 0.96}, {\"color\": \"other\", \"confidence\": 0.22}, {\"color\": \"gray\", \"confidence\": 0.21}, {\"color\": \"red\", \"confidence\": 0.09}, {\"color\": \"blond\", \"confidence\": 0.08}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"c628577a-d681-42d3-91e1-c637ff00332f\", \"faceRectangle\": {\"top\": 69, \"left\": 445, \"width\": 52, \"height\": 52}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": 0.2, \"roll\": 2.6, \"yaw\": 1.5}, \"gender\": \"female\", \"age\": 23.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.0}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.56}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.33}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.11, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"brown\", \"confidence\": 0.81}, {\"color\": \"other\", \"confidence\": 0.41}, {\"color\": \"gray\", \"confidence\": 0.37}, {\"color\": \"blond\", \"confidence\": 0.03}, {\"color\": \"red\", \"confidence\": 0.02}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"68d2e063-ad1b-45b4-8dac-896f722295ce\", \"faceRectangle\": {\"top\": 95, \"left\": 238, \"width\": 51, \"height\": 51}, \"faceAttributes\": {\"smile\": 0.981, \"headPose\": {\"pitch\": 7.2, \"roll\": 1.2, \"yaw\": 2.6}, \"gender\": \"female\", \"age\": 18.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"ReadingGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.981, \"neutral\": 0.019, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.07}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.69}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.48}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [{\"type\": \"glasses\", \"confidence\": 1.0}], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.11, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 0.98}, {\"color\": \"brown\", \"confidence\": 0.86}, {\"color\": \"other\", \"confidence\": 0.4}, {\"color\": \"gray\", \"confidence\": 0.36}, {\"color\": \"blond\", \"confidence\": 0.09}, {\"color\": \"red\", \"confidence\": 0.08}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"6af5fc6e-2c13-477b-8e13-8bc43c4e07cb\", \"faceRectangle\": {\"top\": 94, \"left\": 540, \"width\": 48, \"height\": 48}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": -7.7, \"roll\": 5.0, \"yaw\": 6.9}, \"gender\": \"female\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.08}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.65}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.08}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.1, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"brown\", \"confidence\": 0.88}, {\"color\": \"other\", \"confidence\": 0.35}, {\"color\": \"gray\", \"confidence\": 0.33}, {\"color\": \"blond\", \"confidence\": 0.05}, {\"color\": \"red\", \"confidence\": 0.04}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"f212f71663134b73bdec4a5a9aece2f0\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-hjq.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'http://newmedia.nfu.edu.cn/wcy/wp-content/uploads/2018/04/post_20180424__NFU_DoraHacks_imoji%E5%9B%A2%E9%98%9F.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 json转译\n",
    "\n",
    "> * bytes ---> json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': 'cac77619-4e43-4564-8d56-cb0283c3b3a4',\n",
       "  'faceRectangle': {'top': 118, 'left': 144, 'width': 88, 'height': 88},\n",
       "  'faceAttributes': {'smile': 0.813,\n",
       "   'headPose': {'pitch': -3.4, 'roll': 1.4, 'yaw': 9.4},\n",
       "   'gender': 'male',\n",
       "   'age': 19.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.003,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.813,\n",
       "    'neutral': 0.184,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.21},\n",
       "   'exposure': {'exposureLevel': 'overExposure', 'value': 0.81},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.01},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.18,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 0.95},\n",
       "     {'color': 'black', 'confidence': 0.93},\n",
       "     {'color': 'other', 'confidence': 0.23},\n",
       "     {'color': 'blond', 'confidence': 0.23},\n",
       "     {'color': 'gray', 'confidence': 0.21},\n",
       "     {'color': 'red', 'confidence': 0.15},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': 'ab8b0b55-ff58-477b-82fd-a822c453403e',\n",
       "  'faceRectangle': {'top': 117, 'left': 376, 'width': 64, 'height': 64},\n",
       "  'faceAttributes': {'smile': 0.456,\n",
       "   'headPose': {'pitch': -1.1, 'roll': -0.2, 'yaw': 6.6},\n",
       "   'gender': 'female',\n",
       "   'age': 22.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.001,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.456,\n",
       "    'neutral': 0.542,\n",
       "    'sadness': 0.001,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.16},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.58},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.01},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.08,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'other', 'confidence': 0.63},\n",
       "     {'color': 'brown', 'confidence': 0.46},\n",
       "     {'color': 'gray', 'confidence': 0.39},\n",
       "     {'color': 'blond', 'confidence': 0.03},\n",
       "     {'color': 'red', 'confidence': 0.01},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a',\n",
       "  'faceRectangle': {'top': 41, 'left': 676, 'width': 52, 'height': 52},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': 3.0, 'roll': -1.3, 'yaw': -0.6},\n",
       "   'gender': 'male',\n",
       "   'age': 25.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'ReadingGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.0},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.66},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.07},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [{'type': 'glasses', 'confidence': 1.0}],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.06,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.98},\n",
       "     {'color': 'brown', 'confidence': 0.96},\n",
       "     {'color': 'other', 'confidence': 0.22},\n",
       "     {'color': 'gray', 'confidence': 0.21},\n",
       "     {'color': 'red', 'confidence': 0.09},\n",
       "     {'color': 'blond', 'confidence': 0.08},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': 'c628577a-d681-42d3-91e1-c637ff00332f',\n",
       "  'faceRectangle': {'top': 69, 'left': 445, 'width': 52, 'height': 52},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': 0.2, 'roll': 2.6, 'yaw': 1.5},\n",
       "   'gender': 'female',\n",
       "   'age': 23.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.0},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.56},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.33},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.11,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'brown', 'confidence': 0.81},\n",
       "     {'color': 'other', 'confidence': 0.41},\n",
       "     {'color': 'gray', 'confidence': 0.37},\n",
       "     {'color': 'blond', 'confidence': 0.03},\n",
       "     {'color': 'red', 'confidence': 0.02},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '68d2e063-ad1b-45b4-8dac-896f722295ce',\n",
       "  'faceRectangle': {'top': 95, 'left': 238, 'width': 51, 'height': 51},\n",
       "  'faceAttributes': {'smile': 0.981,\n",
       "   'headPose': {'pitch': 7.2, 'roll': 1.2, 'yaw': 2.6},\n",
       "   'gender': 'female',\n",
       "   'age': 18.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'ReadingGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.981,\n",
       "    'neutral': 0.019,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.07},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.69},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.48},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [{'type': 'glasses', 'confidence': 1.0}],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.11,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.98},\n",
       "     {'color': 'brown', 'confidence': 0.86},\n",
       "     {'color': 'other', 'confidence': 0.4},\n",
       "     {'color': 'gray', 'confidence': 0.36},\n",
       "     {'color': 'blond', 'confidence': 0.09},\n",
       "     {'color': 'red', 'confidence': 0.08},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '6af5fc6e-2c13-477b-8e13-8bc43c4e07cb',\n",
       "  'faceRectangle': {'top': 94, 'left': 540, 'width': 48, 'height': 48},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': -7.7, 'roll': 5.0, 'yaw': 6.9},\n",
       "   'gender': 'female',\n",
       "   'age': 19.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.08},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.65},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.08},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.1,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'brown', 'confidence': 0.88},\n",
       "     {'color': 'other', 'confidence': 0.35},\n",
       "     {'color': 'gray', 'confidence': 0.33},\n",
       "     {'color': 'blond', 'confidence': 0.05},\n",
       "     {'color': 'red', 'confidence': 0.04},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cac77619-4e43-4564-8d56-cb0283c3b3a4</td>\n",
       "      <td>118</td>\n",
       "      <td>144</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>0.813</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.18</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 0.95}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ab8b0b55-ff58-477b-82fd-a822c453403e</td>\n",
       "      <td>117</td>\n",
       "      <td>376</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0.456</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a</td>\n",
       "      <td>41</td>\n",
       "      <td>676</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c628577a-d681-42d3-91e1-c637ff00332f</td>\n",
       "      <td>69</td>\n",
       "      <td>445</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68d2e063-ad1b-45b4-8dac-896f722295ce</td>\n",
       "      <td>95</td>\n",
       "      <td>238</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>0.981</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6af5fc6e-2c13-477b-8e13-8bc43c4e07cb</td>\n",
       "      <td>94</td>\n",
       "      <td>540</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  cac77619-4e43-4564-8d56-cb0283c3b3a4                118   \n",
       "1  ab8b0b55-ff58-477b-82fd-a822c453403e                117   \n",
       "2  8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a                 41   \n",
       "3  c628577a-d681-42d3-91e1-c637ff00332f                 69   \n",
       "4  68d2e063-ad1b-45b4-8dac-896f722295ce                 95   \n",
       "5  6af5fc6e-2c13-477b-8e13-8bc43c4e07cb                 94   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                 144                   88                    88   \n",
       "1                 376                   64                    64   \n",
       "2                 676                   52                    52   \n",
       "3                 445                   52                    52   \n",
       "4                 238                   51                    51   \n",
       "5                 540                   48                    48   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                 0.813                           -3.4   \n",
       "1                 0.456                           -1.1   \n",
       "2                 1.000                            3.0   \n",
       "3                 1.000                            0.2   \n",
       "4                 0.981                            7.2   \n",
       "5                 1.000                           -7.7   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                           1.4                          9.4   \n",
       "1                          -0.2                          6.6   \n",
       "2                          -1.3                         -0.6   \n",
       "3                           2.6                          1.5   \n",
       "4                           1.2                          2.6   \n",
       "5                           5.0                          6.9   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                  male  ...                        0.01   \n",
       "1                female  ...                        0.01   \n",
       "2                  male  ...                        0.07   \n",
       "3                female  ...                        0.33   \n",
       "4                female  ...                        0.48   \n",
       "5                female  ...                        0.08   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                            False                            False   \n",
       "1                            False                             True   \n",
       "2                            False                            False   \n",
       "3                            False                            False   \n",
       "4                            False                            False   \n",
       "5                            False                            False   \n",
       "\n",
       "                 faceAttributes.accessories  \\\n",
       "0                                        []   \n",
       "1                                        []   \n",
       "2  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "3                                        []   \n",
       "4  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "5                                        []   \n",
       "\n",
       "  faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                                     False   \n",
       "1                                     False   \n",
       "2                                     False   \n",
       "3                                     False   \n",
       "4                                     False   \n",
       "5                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "1                                 False   \n",
       "2                                 False   \n",
       "3                                 False   \n",
       "4                                 False   \n",
       "5                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.18   \n",
       "1                                   False                      0.08   \n",
       "2                                   False                      0.06   \n",
       "3                                   False                      0.11   \n",
       "4                                   False                      0.11   \n",
       "5                                   False                      0.10   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "1                          False   \n",
       "2                          False   \n",
       "3                          False   \n",
       "4                          False   \n",
       "5                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'brown', 'confidence': 0.95}, {'col...  \n",
       "1  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "2  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "3  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "4  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "5  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "\n",
       "[6 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-4 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cac77619-4e43-4564-8d56-cb0283c3b3a4',\n",
       " 'ab8b0b55-ff58-477b-82fd-a822c453403e',\n",
       " '8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a',\n",
       " 'c628577a-d681-42d3-91e1-c637ff00332f',\n",
       " '68d2e063-ad1b-45b4-8dac-896f722295ce',\n",
       " '6af5fc6e-2c13-477b-8e13-8bc43c4e07cb']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cac77619-4e43-4564-8d56-cb0283c3b3a4</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.184</td>\n",
       "      <td>19.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ab8b0b55-ff58-477b-82fd-a822c453403e</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.542</td>\n",
       "      <td>22.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c628577a-d681-42d3-91e1-c637ff00332f</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68d2e063-ad1b-45b4-8dac-896f722295ce</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.019</td>\n",
       "      <td>18.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6af5fc6e-2c13-477b-8e13-8bc43c4e07cb</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  cac77619-4e43-4564-8d56-cb0283c3b3a4              NoGlasses   \n",
       "1  ab8b0b55-ff58-477b-82fd-a822c453403e              NoGlasses   \n",
       "2  8c45a05e-828a-4aa9-82a3-9c1ca1dbd88a         ReadingGlasses   \n",
       "3  c628577a-d681-42d3-91e1-c637ff00332f              NoGlasses   \n",
       "4  68d2e063-ad1b-45b4-8dac-896f722295ce         ReadingGlasses   \n",
       "5  6af5fc6e-2c13-477b-8e13-8bc43c4e07cb              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                           0.184                19.0                  male  \n",
       "1                           0.542                22.0                female  \n",
       "2                           0.000                25.0                  male  \n",
       "3                           0.000                23.0                female  \n",
       "4                           0.019                18.0                female  \n",
       "5                           0.000                19.0                female  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure 认知服务-人脸演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试：人脸验证(难)\n",
    "\n",
    "人脸相似度？有没有试下？\n",
    "\n",
    ">* API人脸文档中最重要的组成部分：\n",
    " >>* Request URL？\n",
    " >>* Http Method？\n",
    " >>* 参数？\n",
    "\n",
    "----\n",
    ">* 不同的人脸对比数据\n",
    "  >>* 人脸验证关键数据？\n",
    "  >>* 根据哪些数据证明是同一个人？\n",
    "    \n",
    "----\n",
    ">* 具体步骤：\n",
    "  >>* 1、Create 请求成功200 返回空字符串\n",
    "  >>* 2、Add face 请求成功200 返回persistedFaceId\n",
    "  >>* 3、Detect 准备 被检测人 人脸的id\n",
    "  >>* 4、Find similars 返回相似置信度\n",
    "  >>* 附加：get查看facelists\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02\n"
     ]
    }
   ],
   "source": [
    "# 字符串拼接练习\n",
    "faceListId = \"zhichao02\"\n",
    "# 1\n",
    "url_01 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/\" + faceListId # string 拼接\n",
    "# 2\n",
    "url_02 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/%s\" %(faceListId)\n",
    "# 3 \n",
    "url_03 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(faceListId)\n",
    "print(url_01,'\\n',url_02,'\\n',url_03)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  list列表\n",
    "# faceListId\n",
    "faceListId =\"xianglin13\" # 学生填写设置人脸列表ID\n",
    "create_facelists_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写 ☆ 注意此条url修改\n",
    "subscription_key = \"eaa7f2d3c7e4456fbcc2884e463e77c0\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key ,\n",
    "}\n",
    "data = {\n",
    "    \"name\": \"xinaglin13\",\n",
    "    \"userData\": \"一人\",\n",
    "    \"recognitionModel\": \"recognition_03\",\n",
    "    \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url =  \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写\n",
    "r_get_facelist =requests.get(get_facelist_url.format(faceListId),headers=headers,params=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'xianglin13',\n",
       " 'name': 'xinaglin13',\n",
       " 'userData': '一人'}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add face 请求成功200 返回persistedFaceId\n",
    "> 我们通过上面的步骤建好了一个脸的列表，接下来我们要给这个列表添加脸了！把我们想要对比的脸存进列表吧\n",
    "- [添加人脸进列表api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "add_face_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedfaces\"\n",
    "\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = \"http://huangjieqi.gitee.io/picture_storage/EdisonQXF.jpg\"\n",
    "\n",
    "params_add_face={\n",
    "    \"userData\":\"huangjieqi\",\n",
    "    \"faceListId\":\"xianglin11\",\n",
    "    \"detectionModel\":\"detection_02\",\n",
    "}\n",
    "\n",
    "r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '8564084d-1942-40fd-9480-a396d8be9992',\n",
       "   'userData': 'huangjieqi'}],\n",
       " 'faceListId': 'xianglin13',\n",
       " 'name': 'xinaglin13',\n",
       " 'userData': '一人'}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展内容，封装成函数方便多次使用 *\n",
    "> 我们要添加多张脸，但是为了减少代码量，我们可以把代码封装成函数，避免每次都要写一大堆代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = img_url\n",
    "\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code#返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Autumnhui.jpg\",\"丘天惠\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/L-Tony-info.jpg\",\"林嘉茵\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/TLINGP.jpg\",\"汤玲萍\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/WenYanZeng.jpg\",\"曾雯燕\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/XIEIC.jpg\",\"谢依希\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/YuecongYang.png\",\"杨悦聪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Zoezhouyu.jpg\",\"周雨\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/crayon-heimi.jpg\",\"刘瑜鹏\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/jiayichen.jpg\",\"陈嘉仪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/kg2000.jpg\",\"徐旖芊\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuxinrujiayou.jpg\",\"刘心如\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuyu19.png\",\"刘宇\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/ltco.jpg\",\"李婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lucaszy.jpg\",\"黄智毅\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/pingzi0211.jpg\",\"黄慧文\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/shmimy-cn.jpg\",\"张铭睿\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/yichenting.jpg\",\"陈婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/coco022.jpg\",\"洪可凡\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lujizhi.png\",\"卢继志\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/zzlhyy.jpg\",\"张梓乐\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '8564084d-1942-40fd-9480-a396d8be9992',\n",
       "   'userData': 'huangjieqi'},\n",
       "  {'persistedFaceId': 'cd3b5f20-fbb9-4e9b-9d6a-23d91f4f3c77',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '7dc9fb2a-0e05-443c-99cb-672598e0183d',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'c7950674-efc0-48e8-b267-b7acf12506b6',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '943988c2-c3e9-433f-8dca-09c34bc301dd',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'ae746b8a-720a-4097-86f9-906af4cb8e19',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'e299fcb0-1701-448f-849d-b8fd332e499f',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '76319b13-4c56-4930-abac-42f2bd9b7f18',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '73b93a31-413a-44e7-ba8a-98ee66b60433',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '285c843f-87d9-45ba-bcf6-8a6733952ed8',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '145fd35f-eda2-416d-8552-0740f0034210',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '2e879b1a-f855-44df-aada-8c944dae33d9',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'e9d862ca-ce9c-4466-b6a0-e3ad7a900af1',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '58b37a67-31b3-4d37-b1d2-3744be740e06',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': 'c504ff33-9d39-4244-9887-fb329dd0dd61',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '119bdf7e-5b2c-4157-a5fb-5d3ce2909228',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '13a06347-64ed-4e29-99d9-b50f1413fe95',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '88fdec30-61a8-41ea-9112-82ce4cb0c69e',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '5f5c1fdb-9ee3-4ef4-bf95-3c85b88a24a0',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'b0ffa4c8-fe42-4ad0-88ff-6569990918a1',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '04fd7444-f27f-40e0-972b-5d248c9e7e06',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '0c6b04e2-9896-4bc5-acea-8b0b4a152723',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '1b7fe9ef-ffac-4906-8240-76d5f7c1e1ab',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '59897fd4-d8a0-4b03-9b2e-57482b34564c',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'a556f59c-c34b-4c77-9096-3fd648c40974',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'b26cf1a3-e37e-41d2-97dd-1a7b850c4e68',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '41ec7126-52c2-4018-bba1-e36b54f5e1c8',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '43dd9c65-886d-412c-b79c-5e412b9c3872',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '117c74c3-4262-4b3b-b3fe-de191b23b356',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '875f553a-140a-4209-9c73-ff61831ee94a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '066eb9a4-2276-402c-9e4f-b338c42d92a5',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '306b9d0b-45b4-4a0a-b102-26729ff98896',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '7c641db5-12c1-424e-8101-4c3813e9b99c',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': 'e52a9b81-014d-48cd-96a4-692144f2e4e8',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '1ab9e078-030c-41ee-bfaa-30316f5c140f',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '30aacb7e-6a35-4654-ada2-78dc0a5f4420',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'd304cc96-9310-440c-bb1d-ef7527980384',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '7133aaa3-3bee-45f8-b2f3-076a6a759497',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '177405f3-54e3-4ca9-9566-984aae3508de',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'f81ac60d-3fc0-4c22-9a9b-e73f65d44d68',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '1c5da873-0661-48ca-b142-ab05b86450b9',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'c3927e3f-8cec-46d1-8e1e-b168e539003b',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '954247e1-9341-4ee6-9462-ac11aa8ae305',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'ef95a0f1-9b8c-4d5e-b8d7-6e29e0d1e713',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '00cef95d-423b-4340-b1f2-fe00f3720d78',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '17026d22-cd8b-42f2-a025-fe1f3c6ad231',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '6894fac5-1365-49d7-aef7-58be4d9dc8d0',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'eb057327-87d6-4f2b-a6c9-f879df047507',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '92f83a93-b32b-444f-b6d4-47caf1f6a813',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'fb3d28a6-cafc-4262-96c5-896fd00f1966',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '22f6bd7f-a267-4846-a19c-99177c1802b4',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '951de399-6f91-4ff4-a2e1-7a036d461ceb',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': 'd1984c5e-73b6-4154-9be6-d0a00d1bbdd4',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '232c0ffe-26ef-48aa-8662-353094627ce8',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '2db11e8c-4060-4a6e-b20f-df0a1b8a1034',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '578b04fb-ab0b-4858-8804-cb01b80a3180',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '9abe8e2a-0841-4c8f-9bcf-bb81675f2a41',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '33d84a6b-b600-4496-9c78-7a082d50c4bc',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'xianglin13',\n",
       " 'name': 'xinaglin13',\n",
       " 'userData': '一人'}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7dc9fb2a-0e05-443c-99cb-672598e0183d\n",
      "1b7fe9ef-ffac-4906-8240-76d5f7c1e1ab\n",
      "f81ac60d-3fc0-4c22-9a9b-e73f65d44d68\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'f81ac60d-3fc0-4c22-9a9b-e73f65d44d68'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键/值\n",
    "facelists = ['persistedFaces']\n",
    "for item in faceId:\n",
    "#     print(item)\n",
    "    if item['userData'] == '林嘉茵':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '8564084d-1942-40fd-9480-a396d8be9992',\n",
       "  'userData': 'huangjieqi'},\n",
       " {'persistedFaceId': 'cd3b5f20-fbb9-4e9b-9d6a-23d91f4f3c77',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '7dc9fb2a-0e05-443c-99cb-672598e0183d',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': 'c7950674-efc0-48e8-b267-b7acf12506b6',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '943988c2-c3e9-433f-8dca-09c34bc301dd',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'ae746b8a-720a-4097-86f9-906af4cb8e19',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': 'e299fcb0-1701-448f-849d-b8fd332e499f',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '76319b13-4c56-4930-abac-42f2bd9b7f18', 'userData': '周雨'},\n",
       " {'persistedFaceId': '73b93a31-413a-44e7-ba8a-98ee66b60433',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '285c843f-87d9-45ba-bcf6-8a6733952ed8',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '145fd35f-eda2-416d-8552-0740f0034210',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '2e879b1a-f855-44df-aada-8c944dae33d9',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': 'e9d862ca-ce9c-4466-b6a0-e3ad7a900af1', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '58b37a67-31b3-4d37-b1d2-3744be740e06', 'userData': '李婷'},\n",
       " {'persistedFaceId': 'c504ff33-9d39-4244-9887-fb329dd0dd61',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '119bdf7e-5b2c-4157-a5fb-5d3ce2909228',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '13a06347-64ed-4e29-99d9-b50f1413fe95',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '88fdec30-61a8-41ea-9112-82ce4cb0c69e', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '5f5c1fdb-9ee3-4ef4-bf95-3c85b88a24a0',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'b0ffa4c8-fe42-4ad0-88ff-6569990918a1',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '04fd7444-f27f-40e0-972b-5d248c9e7e06',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '0c6b04e2-9896-4bc5-acea-8b0b4a152723',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '1b7fe9ef-ffac-4906-8240-76d5f7c1e1ab',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '59897fd4-d8a0-4b03-9b2e-57482b34564c',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': 'a556f59c-c34b-4c77-9096-3fd648c40974',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'b26cf1a3-e37e-41d2-97dd-1a7b850c4e68',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '41ec7126-52c2-4018-bba1-e36b54f5e1c8',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '43dd9c65-886d-412c-b79c-5e412b9c3872', 'userData': '周雨'},\n",
       " {'persistedFaceId': '117c74c3-4262-4b3b-b3fe-de191b23b356',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '875f553a-140a-4209-9c73-ff61831ee94a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '066eb9a4-2276-402c-9e4f-b338c42d92a5',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '306b9d0b-45b4-4a0a-b102-26729ff98896',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '7c641db5-12c1-424e-8101-4c3813e9b99c', 'userData': '刘宇'},\n",
       " {'persistedFaceId': 'e52a9b81-014d-48cd-96a4-692144f2e4e8', 'userData': '李婷'},\n",
       " {'persistedFaceId': '1ab9e078-030c-41ee-bfaa-30316f5c140f',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '30aacb7e-6a35-4654-ada2-78dc0a5f4420',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': 'd304cc96-9310-440c-bb1d-ef7527980384',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '7133aaa3-3bee-45f8-b2f3-076a6a759497', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '177405f3-54e3-4ca9-9566-984aae3508de',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'f81ac60d-3fc0-4c22-9a9b-e73f65d44d68',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '1c5da873-0661-48ca-b142-ab05b86450b9',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': 'c3927e3f-8cec-46d1-8e1e-b168e539003b',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '954247e1-9341-4ee6-9462-ac11aa8ae305',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': 'ef95a0f1-9b8c-4d5e-b8d7-6e29e0d1e713',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '00cef95d-423b-4340-b1f2-fe00f3720d78', 'userData': '周雨'},\n",
       " {'persistedFaceId': '17026d22-cd8b-42f2-a025-fe1f3c6ad231',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '6894fac5-1365-49d7-aef7-58be4d9dc8d0',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': 'eb057327-87d6-4f2b-a6c9-f879df047507',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '92f83a93-b32b-444f-b6d4-47caf1f6a813',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': 'fb3d28a6-cafc-4262-96c5-896fd00f1966', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '22f6bd7f-a267-4846-a19c-99177c1802b4', 'userData': '李婷'},\n",
       " {'persistedFaceId': '951de399-6f91-4ff4-a2e1-7a036d461ceb',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': 'd1984c5e-73b6-4154-9be6-d0a00d1bbdd4',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '232c0ffe-26ef-48aa-8662-353094627ce8',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '2db11e8c-4060-4a6e-b20f-df0a1b8a1034', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '578b04fb-ab0b-4858-8804-cb01b80a3180',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '9abe8e2a-0841-4c8f-9bcf-bb81675f2a41',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '33d84a6b-b600-4496-9c78-7a082d50c4bc',\n",
       "  'userData': '张梓乐'}]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c504ff33-9d39-4244-9887-fb329dd0dd61\n",
      "04fd7444-f27f-40e0-972b-5d248c9e7e06\n",
      "1ab9e078-030c-41ee-bfaa-30316f5c140f\n",
      "951de399-6f91-4ff4-a2e1-7a036d461ceb\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'951de399-6f91-4ff4-a2e1-7a036d461ceb'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in faceId:\n",
    "#     print(item)\n",
    "    if item['userData'] == '黄智毅':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delate face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = \"xianglin13\"\n",
    "delete_face_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedfaces/{}\"\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\n",
    "\n",
    "\n",
    "persistedFaceId =  r_add_face.json()['persistedFaceId']\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId),headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_delete_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': 'cd3b5f20-fbb9-4e9b-9d6a-23d91f4f3c77',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '7dc9fb2a-0e05-443c-99cb-672598e0183d',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'c7950674-efc0-48e8-b267-b7acf12506b6',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '943988c2-c3e9-433f-8dca-09c34bc301dd',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'ae746b8a-720a-4097-86f9-906af4cb8e19',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'e299fcb0-1701-448f-849d-b8fd332e499f',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '76319b13-4c56-4930-abac-42f2bd9b7f18',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '73b93a31-413a-44e7-ba8a-98ee66b60433',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '285c843f-87d9-45ba-bcf6-8a6733952ed8',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '145fd35f-eda2-416d-8552-0740f0034210',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '2e879b1a-f855-44df-aada-8c944dae33d9',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'e9d862ca-ce9c-4466-b6a0-e3ad7a900af1',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '58b37a67-31b3-4d37-b1d2-3744be740e06',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': 'c504ff33-9d39-4244-9887-fb329dd0dd61',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '119bdf7e-5b2c-4157-a5fb-5d3ce2909228',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '13a06347-64ed-4e29-99d9-b50f1413fe95',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '88fdec30-61a8-41ea-9112-82ce4cb0c69e',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '5f5c1fdb-9ee3-4ef4-bf95-3c85b88a24a0',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'b0ffa4c8-fe42-4ad0-88ff-6569990918a1',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '04fd7444-f27f-40e0-972b-5d248c9e7e06',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '0c6b04e2-9896-4bc5-acea-8b0b4a152723',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '1b7fe9ef-ffac-4906-8240-76d5f7c1e1ab',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '59897fd4-d8a0-4b03-9b2e-57482b34564c',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'a556f59c-c34b-4c77-9096-3fd648c40974',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'b26cf1a3-e37e-41d2-97dd-1a7b850c4e68',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '41ec7126-52c2-4018-bba1-e36b54f5e1c8',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '43dd9c65-886d-412c-b79c-5e412b9c3872',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '117c74c3-4262-4b3b-b3fe-de191b23b356',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '875f553a-140a-4209-9c73-ff61831ee94a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '066eb9a4-2276-402c-9e4f-b338c42d92a5',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '306b9d0b-45b4-4a0a-b102-26729ff98896',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '7c641db5-12c1-424e-8101-4c3813e9b99c',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': 'e52a9b81-014d-48cd-96a4-692144f2e4e8',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '1ab9e078-030c-41ee-bfaa-30316f5c140f',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '30aacb7e-6a35-4654-ada2-78dc0a5f4420',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'd304cc96-9310-440c-bb1d-ef7527980384',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '7133aaa3-3bee-45f8-b2f3-076a6a759497',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '177405f3-54e3-4ca9-9566-984aae3508de',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'f81ac60d-3fc0-4c22-9a9b-e73f65d44d68',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '1c5da873-0661-48ca-b142-ab05b86450b9',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'c3927e3f-8cec-46d1-8e1e-b168e539003b',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '954247e1-9341-4ee6-9462-ac11aa8ae305',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'ef95a0f1-9b8c-4d5e-b8d7-6e29e0d1e713',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '00cef95d-423b-4340-b1f2-fe00f3720d78',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '17026d22-cd8b-42f2-a025-fe1f3c6ad231',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '6894fac5-1365-49d7-aef7-58be4d9dc8d0',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'eb057327-87d6-4f2b-a6c9-f879df047507',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '92f83a93-b32b-444f-b6d4-47caf1f6a813',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'fb3d28a6-cafc-4262-96c5-896fd00f1966',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '22f6bd7f-a267-4846-a19c-99177c1802b4',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '951de399-6f91-4ff4-a2e1-7a036d461ceb',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': 'd1984c5e-73b6-4154-9be6-d0a00d1bbdd4',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '232c0ffe-26ef-48aa-8662-353094627ce8',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '2db11e8c-4060-4a6e-b20f-df0a1b8a1034',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '578b04fb-ab0b-4858-8804-cb01b80a3180',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '9abe8e2a-0841-4c8f-9bcf-bb81675f2a41',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '33d84a6b-b600-4496-9c78-7a082d50c4bc',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'xianglin13',\n",
       " 'name': 'xinaglin13',\n",
       " 'userData': '一人'}"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api2.cognitiveservices.azure.com//face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find similars 返回相似置信度\n",
    "- [监测人脸相似度api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/face/findsimilar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '28f8bce4-92cf-4dc6-aed3-b081018273a8',\n",
       "  'faceRectangle': {'top': 189, 'left': 200, 'width': 304, 'height': 371}}]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api2.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = 'http://huangjieqi.gitee.io/picture_storage/hjq.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_02',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': '',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->字符串\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "findsimilars_url = \"https://api2.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "\n",
    "# 请求正文 faceId需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"28f8bce4-92cf-4dc6-aed3-b081018273a8\",#取上方的faceID\n",
    "    \"faceListId\": \"xianglin11\",\n",
    "#     \"faceIds\":faceId_02,\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchFace\"#matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': 'c60a2908-37bf-421c-ab05-400d685c544f',\n",
       "  'confidence': 0.32933},\n",
       " {'persistedFaceId': 'a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f',\n",
       "  'confidence': 0.22081},\n",
       " {'persistedFaceId': '08258ba7-efd3-47d6-a6ec-f3e9d54864bd',\n",
       "  'confidence': 0.17546},\n",
       " {'persistedFaceId': '4727a022-5270-4f2b-920b-1aee8f46e456',\n",
       "  'confidence': 0.17214},\n",
       " {'persistedFaceId': 'f76b0d25-28db-4cc6-a91d-d0d3f2a836f2',\n",
       "  'confidence': 0.11838},\n",
       " {'persistedFaceId': '535c9682-53de-4501-b8b5-8202a52bafe0',\n",
       "  'confidence': 0.10092},\n",
       " {'persistedFaceId': 'c72a1d33-dc7b-4120-a061-ee896312bdcb',\n",
       "  'confidence': 0.10023},\n",
       " {'persistedFaceId': '729931a6-d352-40b4-9d9c-50757be75673',\n",
       "  'confidence': 0.10018},\n",
       " {'persistedFaceId': '981030ff-5230-49a1-8c4e-a2a853eb6cc1',\n",
       "  'confidence': 0.09808},\n",
       " {'persistedFaceId': '2dae29a4-c487-461b-9d21-cca828bf1d58',\n",
       "  'confidence': 0.09421}]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>981030ff-5230-49a1-8c4e-a2a853eb6cc1</td>\n",
       "      <td>huangjieqi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d7aabd35-3b75-480d-b2c8-6d99e4ea5908</td>\n",
       "      <td>丘天惠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>535c9682-53de-4501-b8b5-8202a52bafe0</td>\n",
       "      <td>林嘉茵</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c36e214e-62ea-4157-bb68-6a85f087d09b</td>\n",
       "      <td>汤玲萍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6b90ef74-4f76-4d3a-80fa-106c33b8599c</td>\n",
       "      <td>曾雯燕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6cc8ba95-9410-4df0-88f2-0d2b355e66cc</td>\n",
       "      <td>谢依希</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a1df00ed-0052-4e12-bc96-7df7c9fdcff0</td>\n",
       "      <td>杨悦聪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>294d5b34-8103-4f3d-bed2-dbe92e338c99</td>\n",
       "      <td>周雨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3b22f592-6e22-43be-9dc8-c12b011c159a</td>\n",
       "      <td>刘瑜鹏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>08258ba7-efd3-47d6-a6ec-f3e9d54864bd</td>\n",
       "      <td>陈嘉仪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c60a2908-37bf-421c-ab05-400d685c544f</td>\n",
       "      <td>徐旖芊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>f76b0d25-28db-4cc6-a91d-d0d3f2a836f2</td>\n",
       "      <td>刘心如</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7b5b3918-9117-4881-9b8e-9e1abf5cec81</td>\n",
       "      <td>刘宇</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f</td>\n",
       "      <td>李婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2dae29a4-c487-461b-9d21-cca828bf1d58</td>\n",
       "      <td>黄智毅</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7b83b40e-d728-4758-ae4d-9c4e79956b31</td>\n",
       "      <td>黄慧文</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4727a022-5270-4f2b-920b-1aee8f46e456</td>\n",
       "      <td>张铭睿</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>c72a1d33-dc7b-4120-a061-ee896312bdcb</td>\n",
       "      <td>洪可凡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>83d89e6d-d13c-4a7c-adb9-67a680ead304</td>\n",
       "      <td>卢继志</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>729931a6-d352-40b4-9d9c-50757be75673</td>\n",
       "      <td>张梓乐</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         persistedFaceId    userData\n",
       "0   981030ff-5230-49a1-8c4e-a2a853eb6cc1  huangjieqi\n",
       "1   d7aabd35-3b75-480d-b2c8-6d99e4ea5908         丘天惠\n",
       "2   535c9682-53de-4501-b8b5-8202a52bafe0         林嘉茵\n",
       "3   c36e214e-62ea-4157-bb68-6a85f087d09b         汤玲萍\n",
       "4   6b90ef74-4f76-4d3a-80fa-106c33b8599c         曾雯燕\n",
       "5   6cc8ba95-9410-4df0-88f2-0d2b355e66cc         谢依希\n",
       "6   a1df00ed-0052-4e12-bc96-7df7c9fdcff0         杨悦聪\n",
       "7   294d5b34-8103-4f3d-bed2-dbe92e338c99          周雨\n",
       "8   3b22f592-6e22-43be-9dc8-c12b011c159a         刘瑜鹏\n",
       "9   08258ba7-efd3-47d6-a6ec-f3e9d54864bd         陈嘉仪\n",
       "10  c60a2908-37bf-421c-ab05-400d685c544f         徐旖芊\n",
       "11  f76b0d25-28db-4cc6-a91d-d0d3f2a836f2         刘心如\n",
       "12  7b5b3918-9117-4881-9b8e-9e1abf5cec81          刘宇\n",
       "13  a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f          李婷\n",
       "14  2dae29a4-c487-461b-9d21-cca828bf1d58         黄智毅\n",
       "15  7b83b40e-d728-4758-ae4d-9c4e79956b31         黄慧文\n",
       "16  4727a022-5270-4f2b-920b-1aee8f46e456         张铭睿\n",
       "17  c72a1d33-dc7b-4120-a061-ee896312bdcb         洪可凡\n",
       "18  83d89e6d-d13c-4a7c-adb9-67a680ead304         卢继志\n",
       "19  729931a6-d352-40b4-9d9c-50757be75673         张梓乐"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#facelist里面的数据\n",
    "faceListId_df = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "faceListId_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.32933</td>\n",
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       "      <td>0.17546</td>\n",
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       "      <td>0.17214</td>\n",
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       "      <td>0.11838</td>\n",
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       "      <td>0.10092</td>\n",
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       "      <td>0.10023</td>\n",
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       "      <td>0.09808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2dae29a4-c487-461b-9d21-cca828bf1d58</td>\n",
       "      <td>0.09421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        persistedFaceId  confidence\n",
       "0  c60a2908-37bf-421c-ab05-400d685c544f     0.32933\n",
       "1  a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f     0.22081\n",
       "2  08258ba7-efd3-47d6-a6ec-f3e9d54864bd     0.17546\n",
       "3  4727a022-5270-4f2b-920b-1aee8f46e456     0.17214\n",
       "4  f76b0d25-28db-4cc6-a91d-d0d3f2a836f2     0.11838\n",
       "5  535c9682-53de-4501-b8b5-8202a52bafe0     0.10092\n",
       "6  c72a1d33-dc7b-4120-a061-ee896312bdcb     0.10023\n",
       "7  729931a6-d352-40b4-9d9c-50757be75673     0.10018\n",
       "8  981030ff-5230-49a1-8c4e-a2a853eb6cc1     0.09808\n",
       "9  2dae29a4-c487-461b-9d21-cca828bf1d58     0.09421"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回相似度的数据\n",
    "find_df = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "find_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c60a2908-37bf-421c-ab05-400d685c544f</td>\n",
       "      <td>徐旖芊</td>\n",
       "      <td>0.32933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f</td>\n",
       "      <td>李婷</td>\n",
       "      <td>0.22081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08258ba7-efd3-47d6-a6ec-f3e9d54864bd</td>\n",
       "      <td>陈嘉仪</td>\n",
       "      <td>0.17546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4727a022-5270-4f2b-920b-1aee8f46e456</td>\n",
       "      <td>张铭睿</td>\n",
       "      <td>0.17214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f76b0d25-28db-4cc6-a91d-d0d3f2a836f2</td>\n",
       "      <td>刘心如</td>\n",
       "      <td>0.11838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>535c9682-53de-4501-b8b5-8202a52bafe0</td>\n",
       "      <td>林嘉茵</td>\n",
       "      <td>0.10092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c72a1d33-dc7b-4120-a061-ee896312bdcb</td>\n",
       "      <td>洪可凡</td>\n",
       "      <td>0.10023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>729931a6-d352-40b4-9d9c-50757be75673</td>\n",
       "      <td>张梓乐</td>\n",
       "      <td>0.10018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>981030ff-5230-49a1-8c4e-a2a853eb6cc1</td>\n",
       "      <td>huangjieqi</td>\n",
       "      <td>0.09808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2dae29a4-c487-461b-9d21-cca828bf1d58</td>\n",
       "      <td>黄智毅</td>\n",
       "      <td>0.09421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId    userData  confidence\n",
       "3  c60a2908-37bf-421c-ab05-400d685c544f         徐旖芊     0.32933\n",
       "5  a76a9582-4a5f-4b64-9fc4-5c1ec2ebf77f          李婷     0.22081\n",
       "2  08258ba7-efd3-47d6-a6ec-f3e9d54864bd         陈嘉仪     0.17546\n",
       "7  4727a022-5270-4f2b-920b-1aee8f46e456         张铭睿     0.17214\n",
       "4  f76b0d25-28db-4cc6-a91d-d0d3f2a836f2         刘心如     0.11838\n",
       "1  535c9682-53de-4501-b8b5-8202a52bafe0         林嘉茵     0.10092\n",
       "8  c72a1d33-dc7b-4120-a061-ee896312bdcb         洪可凡     0.10023\n",
       "9  729931a6-d352-40b4-9d9c-50757be75673         张梓乐     0.10018\n",
       "0  981030ff-5230-49a1-8c4e-a2a853eb6cc1  huangjieqi     0.09808\n",
       "6  2dae29a4-c487-461b-9d21-cca828bf1d58         黄智毅     0.09421"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(faceListId_df, find_df,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设计人脸识别门禁/打卡/签到 小程序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face++ FaceSets 实践\n",
    "\n",
    "\n",
    ">* 1. FaceSet Create\n",
    ">* 2. FaceSet GetDetail\n",
    ">* 3. FaceSet AddFace\n",
    ">* 4. FaceSet RemoveFace\n",
    ">* 5. FaceSet Update\n",
    ">* 6. Compare Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_secret = \"BNf2vaUrEtNg6Pps9ZeADVTEaP9p0l4W\"\n",
    "api_key =  \"N-m9Ie2ykfHr7KUQxWz2-a8u7XoNcOoM\" # Replace with a valid Subscription Key here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"xinaglin02\"\n",
    "outer_id = \"00002\"\n",
    "user_data = \"mewgulf，两人\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'time_used': 235,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1602986678,f21d9660-e79f-447f-8cdd-873506686588',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'tags': '',\n",
       " 'time_used': 89,\n",
       " 'user_data': 'mewgulf，两人',\n",
       " 'display_name': 'xinaglin02',\n",
       " 'face_tokens': [],\n",
       " 'face_count': 0,\n",
       " 'request_id': '1602986746,f8862a47-5458-4e25-9228-1a444ce5d874',\n",
       " 'outer_id': '00002'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1602991192,f3da1955-18d5-4e36-961b-429146f76b42',\n",
       " 'time_used': 535,\n",
       " 'faces': [{'face_token': 'b38b563ce76b4b42f60fd397f118d91a',\n",
       "   'face_rectangle': {'top': 241, 'left': 491, 'width': 332, 'height': 332}},\n",
       "  {'face_token': 'e676c1d9702f003a778f46a8f49506ff',\n",
       "   'face_rectangle': {'top': 382, 'left': 196, 'width': 326, 'height': 326}}],\n",
       " 'image_id': 'pOLNrf8y3wtXXWid9YaJYA==',\n",
       " 'face_num': 2}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Detect_url=\"https://api-cn.faceplusplus.com/facepp/v3/detect\"\n",
    "    \n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url':\"https://c-ssl.duitang.com/uploads/blog/202010/06/20201006214219_4f6eb.thumb.1000_0.jpg\"\n",
    "}\n",
    "\n",
    "r = requests.post(Detect_url,params=payload)\n",
    "\n",
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'8846329163fd260005ec2c8e11861953',\n",
    "    'face_tokens':'e676c1d9702f003a778f46a8f49506ff,b38b563ce76b4b42f60fd397f118d91a',\n",
    "}\n",
    "\n",
    "r = requests.post(AddFace_url,params=payload,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'time_used': 554,\n",
       " 'face_count': 2,\n",
       " 'face_added': 1,\n",
       " 'request_id': '1602992518,799bc4c9-34e8-4b8a-b6c0-86f972570866',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'8846329163fd260005ec2c8e11861953',\n",
    "    'face_tokens':'e676c1d9702f003a778f46a8f49506ff,b38b563ce76b4b42f60fd397f118d91a',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'face_removed': 2,\n",
       " 'time_used': 205,\n",
       " 'face_count': 0,\n",
       " 'request_id': '1602992635,cb859dd2-25b6-4769-88c3-294a3151946f',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'8846329163fd260005ec2c8e11861953',\n",
    "    'user_data':\"mewgulf，两人\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'request_id': '1602992709,98d34d81-7de0-44cf-a087-41d75561ed74',\n",
       " 'time_used': 80,\n",
       " 'outer_id': '00002'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "gulf01 = \"https://c-ssl.duitang.com/uploads/blog/202010/06/20201006214221_fe6c1.thumb.1000_0.jpg\"\n",
    "gulf02 = \"https://c-ssl.duitang.com/uploads/blog/202010/06/20201006214151_427b0.thumb.1000_0.jpg\"\n",
    "mew = \"https://c-ssl.duitang.com/uploads/item/202006/12/20200612105348_yawli.thumb.1000_0.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url1':gulf01,\n",
    "    'image_url2':mew\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faces1': [{'face_rectangle': {'width': 344,\n",
       "    'top': 398,\n",
       "    'left': 481,\n",
       "    'height': 344},\n",
       "   'face_token': '626523a2337d23b5bb77314b2c1f03ed'}],\n",
       " 'faces2': [{'face_rectangle': {'width': 236,\n",
       "    'top': 230,\n",
       "    'left': 348,\n",
       "    'height': 236},\n",
       "   'face_token': '8c4c2be24c3b7961a5b0b80880aa0047'}],\n",
       " 'time_used': 969,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-5': 73.975, '1e-4': 69.101},\n",
       " 'confidence': 55.312,\n",
       " 'image_id2': 'z/9tR0PNi+CWrN9zIV30/A==',\n",
       " 'image_id1': 'Wr+dIewOZb8vZQtcPQy4Zg==',\n",
       " 'request_id': '1602992938,e34aa4dc-285c-4b9e-aa6b-a81b2769f168'}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = gulf02\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1602993130,21f352e9-0b96-4132-965e-c037a7ec4bb8',\n",
       " 'time_used': 372,\n",
       " 'faces': [{'face_token': '72e12c093d9cf4a2f624258d5ef9fb68',\n",
       "   'face_rectangle': {'top': 376, 'left': 225, 'width': 332, 'height': 332},\n",
       "   'attributes': {'gender': {'value': 'Male'},\n",
       "    'age': {'value': 23},\n",
       "    'smile': {'value': 3.735, 'threshold': 50.0},\n",
       "    'emotion': {'anger': 0.003,\n",
       "     'disgust': 0.293,\n",
       "     'fear': 0.003,\n",
       "     'happiness': 2.676,\n",
       "     'neutral': 97.011,\n",
       "     'sadness': 0.003,\n",
       "     'surprise': 0.011}}}],\n",
       " 'image_id': 'kiYnPvyIdrnOQZ1A2v8MZA==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加入人脸集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '8846329163fd260005ec2c8e11861953',\n",
       " 'time_used': 590,\n",
       " 'face_count': 2,\n",
       " 'face_added': 1,\n",
       " 'request_id': '1602993216,4b79e938-fb02-49db-81ae-adbad5921a7f',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'8846329163fd260005ec2c8e11861953',\n",
    "    'face_tokens':'72e12c093d9cf4a2f624258d5ef9fb68,626523a2337d23b5bb77314b2c1f03ed',\n",
    "}\n",
    "\n",
    "r = requests.post(AddFace_url,params=payload,json={\"url\":img_url})\n",
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别社会/政治使用思考\n",
    "> 人脸识别的 API比较\n",
    ">> * [Face Off: Confronting Bias in Face Recognition AI](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    ">> * [为什么世界现在需要道德的面部识别](https://www.kairos.com/blog/why-the-world-needs-ethical-facial-recognition-now)\n",
    "    * 到2023年，全球人脸识别市场的价值估计接近100亿美元，复合年增长率为16.8％。市场背后的主要增长动力是监视市场的发展以及生物识别技术领域的政府支出。但是，面部识别的使用在其他领域也有很大贡献，例如帮助企业打击消费者欺诈，满足动态法规遵从性以及提供可获利的客户体验。\n",
    "\n",
    "-----\n",
    "> 人脸识别的偏差及API 政治经济学 \n",
    ">> * [对抗：面对面部识别AI中的偏见](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    "    * 发生了什么？\n",
    "        * “编码注视”或算法偏差的研究: 浅肤色男性的错误率为0.8％ ?  深色皮肤女性的错误率高达34.7％  ? (Microsoft，IBM和Face ++)\n",
    "    * 用户的反应范围从惊奇和赞美到不悦和冒犯\n",
    "        * 我们承认目前提供的种族分类（黑人，白人，亚裔，西班牙裔，其他则不足以代表丰富多样，发展迅速的文化和种族挂毯\n",
    "    * 该怎么办？\n",
    "        * 改善数据\n",
    "        * 寻求持续的反馈    \n",
    "        \n",
    ">>  * [科技行业没有应对面部识别偏见的计划](https://www.theverge.com/2018/7/26/17616290/facial-recognition-ai-bias-benchmark-test) \n",
    ">>> 公司在做什么？(一些高科技大公司支持基准和法规)     \n",
    ">>> 我们如何解决偏见问题？    \n",
    ">>> 偏差不是唯一的问题\n",
    "\n",
    ">> * [面部识别尚未准备好给执法部门使用](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉\n",
    "\n",
    "* 学习并完成所有Azure computer version 的API调用\n",
    "> * 分析远程图像    \n",
    "> * 分析本地图片    \n",
    "> * 生成缩略图    \n",
    "> * 提取印刷体文本和手写文本    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"others_\", \"score\": 0.00390625}, {\"name\": \"people_\", \"score\": 0.70703125, \"detail\": {\"celebrities\": []}}], \"color\": {\"dominantColorForeground\": \"White\", \"dominantColorBackground\": \"Black\", \"dominantColors\": [\"Black\", \"White\", \"Grey\"], \"accentColor\": \"8B2419\", \"isBwImg\": true, \"isBWImg\": true}, \"description\": {\"tags\": [\"person\", \"sitting\", \"woman\", \"photo\", \"table\", \"looking\", \"man\", \"posing\", \"bed\", \"holding\", \"dress\", \"wearing\", \"standing\", \"shirt\", \"phone\", \"laying\", \"cat\"], \"captions\": [{\"text\": \"a person sitting on a bed\", \"confidence\": 0.7685602090633709}]}, \"requestId\": \"6999e7d6-b483-4f25-a6b2-053b68b42774\", \"metadata\": {\"height\": 1592, \"width\": 828, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'categories': [{'name': 'others_', 'score': 0.00390625},\n",
       "  {'name': 'people_', 'score': 0.70703125, 'detail': {'celebrities': []}}],\n",
       " 'color': {'dominantColorForeground': 'White',\n",
       "  'dominantColorBackground': 'Black',\n",
       "  'dominantColors': ['Black', 'White', 'Grey'],\n",
       "  'accentColor': '8B2419',\n",
       "  'isBwImg': True,\n",
       "  'isBWImg': True},\n",
       " 'description': {'tags': ['person',\n",
       "   'sitting',\n",
       "   'woman',\n",
       "   'photo',\n",
       "   'table',\n",
       "   'looking',\n",
       "   'man',\n",
       "   'posing',\n",
       "   'bed',\n",
       "   'holding',\n",
       "   'dress',\n",
       "   'wearing',\n",
       "   'standing',\n",
       "   'shirt',\n",
       "   'phone',\n",
       "   'laying',\n",
       "   'cat'],\n",
       "  'captions': [{'text': 'a person sitting on a bed',\n",
       "    'confidence': 0.7685602090633709}]},\n",
       " 'requestId': '6999e7d6-b483-4f25-a6b2-053b68b42774',\n",
       " 'metadata': {'height': 1592, 'width': 828, 'format': 'Jpeg'}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://gxl.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"754e142aa949439aa27e58b26ae6534c\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://c-ssl.duitang.com/uploads/blog/202010/06/20201006214150_c0e3d.thumb.1000_0.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'people_', 'score': 0.45703125, 'detail': {'celebrities': []}}, {'name': 'people_group', 'score': 0.53125, 'detail': {'celebrities': []}}], 'color': {'dominantColorForeground': 'Black', 'dominantColorBackground': 'Black', 'dominantColors': ['Black'], 'accentColor': '204069', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['person', 'standing', 'man', 'indoor', 'room', 'video', 'playing', 'people', 'young', 'holding', 'game', 'boy', 'front', 'living', 'group', 'wii', 'woman', 'court', 'screen', 'shirt', 'suit', 'board'], 'captions': [{'text': 'a group of people standing in a room', 'confidence': 0.9588301055740119}]}, 'requestId': '98ec03a4-baba-4ca4-95b6-333292b26bf2', 'metadata': {'height': 560, 'width': 400, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "# analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"mewgulf.jpg\"\n",
    "analyze_url = \"https://gxl.cognitiveservices.azure.com/vision/v2.1/analyze\"\n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path,\"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"754e142aa949439aa27e58b26ae6534c\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://gxl.cognitiveservices.azure.com/\" + \"vision/v2.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://c-ssl.duitang.com/uploads/blog/202010/06/20201006214151_2d503.thumb.1000_0.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"754e142aa949439aa27e58b26ae6534c\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取印刷体文本和手写文本 \n",
    "* 假如我们抓取了1000张网页，出了文本信息我们分析以外，还有每个页面的图片的信息，我们可以用提取图片文本的方式，将图片的信息也抓取下来\n",
    "* 我们进抓取了图片，想知道这些图片的内容是什么，也可以用提取文本的方式进行提取\n",
    "* ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"status\": \"running\",\n",
      "    \"createdDateTime\": \"2020-10-20T03:14:14Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-20T03:14:14Z\"\n",
      "}\n",
      "{\n",
      "    \"status\": \"succeeded\",\n",
      "    \"createdDateTime\": \"2020-10-20T03:14:14Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-20T03:14:14Z\",\n",
      "    \"analyzeResult\": {\n",
      "        \"version\": \"3.0.0\",\n",
      "        \"readResults\": [\n",
      "            {\n",
      "                \"page\": 1,\n",
      "                \"angle\": -0.6966,\n",
      "                \"width\": 1000,\n",
      "                \"height\": 1501,\n",
      "                \"unit\": \"pixel\",\n",
      "                \"lines\": [\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            467,\n",
      "                            925,\n",
      "                            528,\n",
      "                            924,\n",
      "                            529,\n",
      "                            936,\n",
      "                            468,\n",
      "                            937\n",
      "                        ],\n",
      "                        \"text\": \"PAINKILLER\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    468,\n",
      "                                    926,\n",
      "                                    529,\n",
      "                                    924,\n",
      "                                    529,\n",
      "                                    936,\n",
      "                                    468,\n",
      "                                    937\n",
      "                                ],\n",
      "                                \"text\": \"PAINKILLER\",\n",
      "                                \"confidence\": 0.973\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            406,\n",
      "                            1470,\n",
      "                            570,\n",
      "                            1468,\n",
      "                            571,\n",
      "                            1493,\n",
      "                            406,\n",
      "                            1495\n",
      "                        ],\n",
      "                        \"text\": \"mcacactus\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    406,\n",
      "                                    1470,\n",
      "                                    570,\n",
      "                                    1468,\n",
      "                                    570,\n",
      "                                    1494,\n",
      "                                    406,\n",
      "                                    1496\n",
      "                                ],\n",
      "                                \"text\": \"mcacactus\",\n",
      "                                \"confidence\": 0.978\n",
      "                            }\n",
      "                        ]\n",
      "                    }\n",
      "                ]\n",
      "            }\n",
      "        ]\n",
      "    }\n",
      "}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "missing_env = False\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "    # endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "# else:\n",
    "    # print(\"From Azure Cognitive Service, retrieve your endpoint and subscription key.\")\n",
    "   #  print(\"\\nSet the COMPUTER_VISION_ENDPOINT environment variable, such as \\\"https://westus2.api.cognitive.microsoft.com\\\".\\n\")\n",
    "   #  missing_env = True\n",
    "\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "   #  subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "   #  print(\"From Azure Cognitive Service, retrieve your endpoint and subscription key.\")\n",
    "   # print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable, such as \\\"1234567890abcdef1234567890abcdef\\\".\\n\")\n",
    "    #missing_env = True\n",
    "\n",
    "#if missing_env:\n",
    "   # print(\"**Restart your shell or IDE for changes to take effect.**\")\n",
    "   # sys.exit()\n",
    "\n",
    "text_recognition_url = \"https://gxl.cognitiveservices.azure.com/vision/v3.0/read/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to recognize.\n",
    "image_url = \"https://c-ssl.duitang.com/uploads/item/202006/12/20200612105348_yawli.thumb.1000_0.jpg\"\n",
    "subscription_key = \"754e142aa949439aa27e58b26ae6534c\"\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(\n",
    "    text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    \n",
    "    print(json.dumps(analysis, indent=4))\n",
    "\n",
    "    time.sleep(1)\n",
    "    if (\"analyzeResult\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"analyzeResult\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"analyzeResult\"][\"readResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三个平台：百度智能云\n",
    "## 失败"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'refresh_token': '25.d2aa480c51ae0b9d1e06ff85be6bb76d.315360000.1918867165.282335-22855644', 'expires_in': 2592000, 'session_key': '9mzdDAE+q/ZZwoJYQdrLXcPcEWiOtkskpO1y+uV2NTGw7wry80FrVBg5gVm05z/KxA5fgOA/rgMlGt/3koCv1t1mSsqm8w==', 'access_token': '24.7410092eac51ae33a2786224b73f8a54.2592000.1606099165.282335-22855644', 'scope': 'public brain_all_scope vis-faceverify_faceverify_h5-face-liveness vis-faceverify_FACE_V3 vis-faceverify_idl_face_merge vis-faceverify_FACE_EFFECT vis-faceverify_face_feature_sdk wise_adapt lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian ApsMisTest_Test权限 vis-classify_flower lpq_开放 cop_helloScope ApsMis_fangdi_permission smartapp_snsapi_base smartapp_mapp_dev_manage iop_autocar oauth_tp_app smartapp_smart_game_openapi oauth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi smartapp_opensource_recapi fake_face_detect_开放Scope vis-ocr_虚拟人物助理 idl-video_虚拟人物助理 smartapp_component', 'session_secret': 'b4b77a866940f3db9437bcbe82f45d85'}\n"
     ]
    }
   ],
   "source": [
    " # encoding:utf-8\n",
    "import requests \n",
    "\n",
    "# client_id 为官网获取的AK， client_secret 为官网获取的SK\n",
    "host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=BRogF97Qkfqtzuh5Q8Ekhiq4&client_secret=DH3uIYICbg34zpPXdnNsTHGnoAgKVhjr'\n",
    "response = requests.get(host)\n",
    "if response:\n",
    "    print(response.json())\n",
    "r = response.json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24.7410092eac51ae33a2786224b73f8a54.2592000.1606099165.282335-22855644'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r['access_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "access_token = r['access_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24.7410092eac51ae33a2786224b73f8a54.2592000.1606099165.282335-22855644'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 222209,\n",
       " 'error_msg': 'face token not exist',\n",
       " 'log_id': 4520110175157,\n",
       " 'timestamp': 1603507200,\n",
       " 'cached': 0,\n",
       " 'result': None}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import base64\n",
    "\n",
    "'''\n",
    "人脸检测与属性分析\n",
    "'''\n",
    "\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/detect\"\n",
    "params = {\"image\": access_token,\n",
    "          \"image_type\": \"FACE_TOKEN\",\n",
    "          \"face_field\":\"faceshape,facetype\"}\n",
    "\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 糊里糊涂地成功"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "Secret_key = 'DH3uIYICbg34zpPXdnNsTHGnoAgKVhjr'\n",
    "API_key = 'BRogF97Qkfqtzuh5Q8Ekhiq4'\n",
    "import requests\n",
    "import base64\n",
    "import matplotlib.pyplot as plt # plt 用于显示图片\n",
    "import matplotlib.image as mpimg # mpimg 用于读取图片\n",
    "#获取access_token\n",
    "def gettoken():\n",
    "    host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=BRogF97Qkfqtzuh5Q8Ekhiq4&client_secret=DH3uIYICbg34zpPXdnNsTHGnoAgKVhjr'\n",
    "    response = requests.get(host)\n",
    "    if response:\n",
    "        return response.json()['access_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getscore(url):\n",
    "    f=open(url,'rb')#二进制读写，转换base64\n",
    "    base64_data = base64.b64encode(f.read())\n",
    "    request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/detect\"\n",
    "    # 参数 \n",
    "    params = {\n",
    "        \"image\":base64_data,\n",
    "        \"image_type\":\"BASE64\",\n",
    "        \"face_field\":\"beauty\"\n",
    "    }\n",
    "    #请求\n",
    "    request_url = request_url + \"?access_token=\" + gettoken()\n",
    "    headers = {'content-type': 'application/json'}\n",
    "    response = requests.post(request_url, data=params, headers=headers)\n",
    "    if response:\n",
    "        print (\"经过百度智能云的AI大数据评分，您的颜值分数为：\",response.json()['result']['face_list'][0]['beauty'])\n",
    "        lena = mpimg.imread(url) \n",
    "        plt.imshow(lena) \n",
    "        plt.axis('off')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "经过百度智能云的AI大数据评分，您的颜值分数为： 50.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "getscore('mewgulf.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习人脸识别和计算机视觉心得体会\n",
    "\n",
    "* （见gitee）链接：https://gitee.com/gaoxianglin/api/blob/master/API%E7%AC%AC%E4%BA%94%E5%91%A8/%E6%84%9F%E6%83%B3.md\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "182.698px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
